Adaptive and virtual reconfigurations for effective dynamic job scheduling in cluster systems

In a cluster system with dynamic load sharing support, a job submission or migration to a workstation is determined by the availability of CPU and memory resources of the workstation at the time. In such a system, a small number of running jobs with unexpectedly large memory allocation requirements may significantly increase the queuing delay times of the rest of jobs with normal memory requirements, slowing down executions of individual jobs and decreasing the system throughput. We call this phenomenon as the job blocking problem because the big jobs block the execution pace of majority jobs in the cluster. We propose a software method incorporating with dynamic load sharing, which adaptively reserves a small set of workstations through virtual cluster reconfiguration to provide special services to the jobs demanding large memory allocations. This policy implies the principle of shortest-remaining-processing-time policy. As soon as the blocking problem is resolved by the reconfiguration, the system will adaptively switch back to the normal load sharing state. We present three contributions in this study. (1) the conditions to cause the job blocking problem; (2) the adaptive software method in a dynamic load sharing system; and (3) trace-driven simulations. We show that our method can effectively improve the cluster computing performance by quickly resolving the job blocking problem. The effectiveness and performance insights are also analytically verified.